Maintaining software code quality of modern business software applications has become challenging for an individual or even for a team. The primary reason for this complexity is that the current software applications are way too complex and consist of millions of lines of code written in diverse programming languages and backed by hundreds of business rules. This situation demands for automation in maintaining software code quality to ensure that implementation of the software application meets quality goals that are important to the organization.
Traditionally, automation in maintaining software code quality is achieved by incorporating various quality audit tools that are driven by one or more rules. However, as the software code base and programming languages evolve over time, the existing one or more rules become obsolete and irrelevant. Also, with modern software development practices, creating and updating these one or more rules has become nearly impossible. Further, a rule based model cannot learn from manual rectifications made in the software codes by experienced software developers.
Therefore, there is a need for a learning based adaptive solution that may predict software code remediation against any coding standard violation in any of the programming languages and may overcome limitations of the rule-based techniques of improvising quality of the software codes.